Vortex beams (VBs), characterized by helical phase front and orbital angular momentum (OAM), have shown perspective potential in improving communication capacity density for providing an additional multiplexing dimension. Here, we propose a diffractive deep neural network (D ² NN) method for OAM mode multiplexing and demultiplexing. By designing the D ² NN model and simulating light propagation through multiple diffractive screens, the phase and amplitude values can be automatically adjusted to manipulate the wavefront of light beams. Training the D ² NN model with mode coupler and separator functions, we convert VBs into target light fields with the diffraction efficiency exceeds 97%, and the mode purities are over 97%. Constructing an OAM multiplexing link, we successfully multiplex and demultiplex two OAM channels that carry 16-QAM signals in simulation, and the demodulated bit-error-rates are below 1×10 ^-4 . It is anticipated that the D ² NN can perform flexible modulation of multiple OAM modes, which may open a new avenue for high-capacity OAM communication and all-optical information processing, etc.

中文翻译：

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用于光轨道角动量复用和解复用的衍射深度神经网络


涡旋光束（VB）以螺旋相位锋和轨道角动量（OAM）为特征，在提高通信容量密度以提供额外的复用维度方面显示出潜在的潜力。在这里，我们提出了一种用于 OAM 模式复用和解复用的衍射深度神经网络 (D ² NN) 方法。通过设计D ² NN模型并模拟光通过多个衍射屏的传播，可以自动调整相位和振幅值以操纵光束的波前。通过模式耦合器和分离器功能训练D ² NN模型，我们将VB转换为目标光场，衍射效率超过97％，模式纯度超过97％。通过构建OAM复用链路，我们在仿真中成功复用和解复用了两个承载16-QAM信号的OAM通道，解调误码率低于1×10 ^-4 。预计D ² NN可以对多种OAM模式进行灵活调制，这可能为大容量OAM通信和全光信息处理等开辟新途径。